An agent-based memetic algorithm (AMA) for solving constrained optimization problems

Abu S.S.M.Barkat Ullah, Ruhul Sarker, David Cormfort, Chris Lokan

Research output: A Conference proceeding or a Chapter in BookConference contribution

26 Citations (Scopus)

Abstract

In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
EditorsKay Chen Tan, Jian Xin Xu, Dipti Srinivasan, Lipo Wang
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages999-1006
Number of pages8
ISBN (Print)1424413400, 9781424413409
DOIs
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sep 200728 Sep 2007

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period25/09/0728/09/07

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